The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we challenge key components of the CVAE. We leverage recent advances in the space of the VAE, the foundation of the CVAE, which show that a simple change in the sampling procedure can greatly benefit performance. We find that unscented sampling, which draws samples from any learned distribution in a deterministic manner, can naturally be better suited to trajectory prediction than potentially dangerous random sampling. We go further and offer additional improvements including a more structured Gaussian mixture latent space, as well as a novel, potentially more expressive way to do inference with CVAEs. We show wide applicability of our models by evaluating them on the INTERACTION prediction dataset, outperforming the state of the art, as well as at the task of image modeling on the CelebA dataset, outperforming the baseline vanilla CVAE. Code is available at https://github.com/boschresearch/cuae-prediction.

翻译：条件变分自编码器（CVAE）是自动驾驶轨迹预测中最广泛使用的模型之一，它将驾驶环境与真实未来轨迹之间的相互影响编码至概率潜空间，并利用该空间生成预测结果。本文对CVAE的核心组件提出了挑战。我们利用变分自编码器（VAE，CVAE的基础模型）的最新进展，发现采样过程中简单的调整即可显著提升性能。研究表明，无迹采样（以确定性方式从任意学习分布中采样）天然比可能带来风险的随机采样更适合轨迹预测。我们进一步提出改进措施：构建更具结构化的高斯混合潜空间，以及一种更具表达潜力的CVAE推理新方法。通过在INTERACTION轨迹预测数据集上超越现有最优方法，以及在CelebA图像建模数据集上超越基线CVAE模型，验证了所提模型的广泛适用性。相关代码已开源至https://github.com/boschresearch/cuae-prediction。